Introduction to Deep Learning with NVIDIA GPUs

Course Overview

Artificial intelligence (AI) is growing exponentially.

We are living in an era where self-driving cars are clocking up millions of miles, with great accuracy, and where Google Deepmind’s AlphaGo beat the world champion at Go – a game where intuition plays a key role.

But as AI advances, the problems become even more complex to solve. Only Deep Learning can solve such complex problems and that’s why it’s at the heart of AI today. While companies like Amazon, Google, and Facebook are pouring billions into Deep Learning projects, what about the rest of us – where do we start?

This course aims to introduce students to Deep Learning as a subject within advanced AI and provides real-life problem sets that can be solved using Deep Learning neural networks.

Learning Objectives

Learn the fundamental concepts in Deep Learning and gain an understanding of the intuition and application of:

Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Self-Organizing Maps

Boltzmann Machines

AutoEncoders

Who Should Attend?

Anyone interested in Deep Learning

Students who have at least high school knowledge in math and who want to start learning Deep Learning

Any intermediate-level enthusiasts who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning

Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets

Any students in college who want to start a career in Data Science

Any data analysts who want to level up in Deep Learning

Any people who are not satisfied with their job and who want to become a Data Scientist

Any people who want to create added value to their business by using powerful Deep Learning tools

Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business

Any Entrepreneur who want to create disruption in an industry using the most cutting edge Deep Learning algorithms

Course Outline

Day 1

1. What is Deep Learning and what are Neural Networks? (30 min)

Deep Learning as a branch of AI

Neural networks and their history and relationship to neurons

Creating a neural network in Python

2. Artificial Neural Networks (ANN) Intuition (60 min)

Understanding the neuron and neuroscience

The activation function (utility function or loss function)

How do NN’s work?

How do NN’s learn?

Gradient descent

Stochastic Gradient descent

Backpropagation

BREAK (15 min)

3. Building an ANN (60 min)

Getting the python libraries

Constructing ANN

Using the bank customer churn dataset

Predicting if customer will leave or not

4. Evaluating Performance of an ANN (60 min)

Evaluating the ANN

Improving the ANN

Tuning the ANN

LUNCH (60 min)

5. Hands-On Exercise (60 min)

Participants will be asked to build the ANN from the previous exercise

Participants will be asked to improve the accuracy of their ANN

6. Convolutional Neural Networks (CNN) Intuition (60 min)

What are CNN’s?

Convolution operation

ReLU Layer

Pooling

Flattening

Full Connection

Softmax and Cross-entropy

BREAK (15 min)

7. Building a CNN (60 min)

Getting the python libraries

Constructing a CNN

Using the Image classification dataset

Predicting the class of an image

Day 2

1. Evaluating Performance of a CNN (60 min)

Evaluating the CNN

Improving the CNN

Tuning the CNN

2. Hands-On Exercise (60 min)

Participants will be asked to build the CNN from the previous exercise

Participants will be asked to improve the accuracy of their CNN

BREAK (15 min)

3. Recurrent Neural Networks (RNN) Intuition (60 min)

What are RNNs?

Vanishing Gradient problem

LSTMs

Practical intuition

LSTM variations

LUNCH (60 min)

4. Building a RNN (60 min)

Getting the python libraries

Constructing RNN

Using the stock prediction dataset

Predicting stock price

5. Evaluating Performance of a RNN (60 min)

Evaluating the RNN

Improving the RNN

Tuning the RNN

6. Hands-On Exercise (60 min)

Participants will be asked to build the RNN from the previous exercise

Participants will be asked to improve the accuracy of their RNN

Day 3

1. Image Classification with DIGITS (120 min)

How to leverage deep neural networks (DNN) within the deep learning workflow

learn about the role of batch size in inference performance as well as virus optimisations that can be made in the inference process

4. Closing

Q & A Session

Prerequisite

Basic high school mathematics

Required Software

Anaconda for Python (version 3.x)

Optionally Sublime Text

Our Speaker

Tarun Sukhani

Tarun Sukhani has 16 years of both academic and industry experience as a data scientist. Starting off as an Enterprise Application Integration (EAI) consultant in the USA, Tarun was involved in a number of integration and ETL projects for a variety of Fortune 500 and Global 1000 clients, such as BP Amoco, Praxair, and GE Medical Systems.

While completing his Master’s degree in Data Warehousing, Data Mining, and Business Intelligence at Loyola University Chicago GSB in 2005, Tarun also worked as a BI consultant for a number of Fortune 500 clients at Revere Consulting, a Chicago-based boutique IT firm focusing on Data Warehousing/Mining projects.

Tarun continues to work within the Business Intelligence space, most recently focusing his time on Deep/Reinforcement Learning projects within the Fintech sector. For this, Tarun Sukhani has worked on parametric statistical modeling as well within the Data Science and Big Data Science space, using tools such as SciPy in Python and R and R/Hadoop for Big Data projects.